class_docstr: Creates a random ill-conditioned Gaussian.
  
  The covariance matrix has eigenvalues sampled from the inverse Gamma
  distribution with the specified shape, and then rotated by a random orthogonal
  matrix.
  
  Note that this function produces reproducible targets, i.e. the constructor
  `seed` argument always needs to be non-`None`.
code: def covariance_eigenvalues(self):
      return self._eigenvalues
  
file_path: probability/spinoffs/inference_gym/inference_gym/targets/ill_conditioned_gaussian.py
imports: import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
  from inference_gym.targets import model
label_desc: Use image node_id 3 for calling the IllConditionedGaussian obj's underlying member method code with example usage: obj.covariance_eigenvalues() and returns: self